We argue that the literature concerning the valuation of non-market, spatially defined goods (such as those provided by the natural environment) is crucially deficient in two respects. First, it fails to employ a theoretically consistent structural model of utility to the separate and hence correct definition of use and non-use values. Second, applications (particularly those using stated preference methods) typically fail to capture the spatially complex distribution of resources and their substitutes within analyses, again leading to error. This paper proposes a new methodology for addressing both issues simultaneously. We combine revealed (travel cost) and stated preference (choice experiment) data within a random utility model formulated from first principles to yield a theoretically consistent distinction between the use and non-use value of improvements in a non-market natural resource. The model is specified to relate both types of value to the attributes of the good in question including the spatial arrangement of the resource under consideration and its substitutes. We test the properties of the model using data simulated from a real world case study examining an improvement of open-access waters to good ecological standards. Through a Monte Carlo experiment we show that both use and non-use parameters can be precisely estimated from a modest sample of observations.